Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 87–118 |
| Number of pages | 32 |
| Journal | Annals of Operations Research |
| Volume | 282 |
| Issue number | 1-2 |
| Early online date | 15 Mar 2018 |
| DOIs | |
| Publication status | Published - Nov 2019 |
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Bibliographical note
The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-018-2808-0Keywords
- Government bond spreads
- Support Vector Regression
- Krill Herd
- Sine Cosine Algorithm
- Eurozone
ASJC Scopus subject areas
- Economics, Econometrics and Finance(all)
Cite this
Forecasting Government Bond Spreads with Heuristic Models : Evidence from the Eurozone Periphery. / Fernandes, Filipa Da Silva ; Stasinakis, Charalampos; Zekaite, Zivile.
In: Annals of Operations Research, Vol. 282, No. 1-2, 11.2019, p. 87–118.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Forecasting Government Bond Spreads with Heuristic Models
T2 - Evidence from the Eurozone Periphery
AU - Fernandes, Filipa Da Silva
AU - Stasinakis, Charalampos
AU - Zekaite, Zivile
N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-018-2808-0
PY - 2019/11
Y1 - 2019/11
N2 - This study investigates the predictability of European long-term government bond spreads through the application of heuristic and metaheuristic support vector regression (SVR) hybrid structures. Genetic, krill herd and sine–cosine algorithms are applied to the parameterization process of the SVR and locally weighted SVR (LSVR) methods. The inputs of the SVR models are selected from a large pool of linear and non-linear individual predictors. The statistical performance of the main models is evaluated against a random walk, an Autoregressive Moving Average, the best individual prediction model and the traditional SVR and LSVR structures. All models are applied to forecast daily and weekly government bond spreads of Greece, Ireland, Italy, Portugal and Spain over the sample period 2000–2017. The results show that the sine–cosine LSVR is outperforming its counterparts in terms of statistical accuracy, while metaheuristic approaches seem to benefit the parameterization process more than the heuristic ones.
AB - This study investigates the predictability of European long-term government bond spreads through the application of heuristic and metaheuristic support vector regression (SVR) hybrid structures. Genetic, krill herd and sine–cosine algorithms are applied to the parameterization process of the SVR and locally weighted SVR (LSVR) methods. The inputs of the SVR models are selected from a large pool of linear and non-linear individual predictors. The statistical performance of the main models is evaluated against a random walk, an Autoregressive Moving Average, the best individual prediction model and the traditional SVR and LSVR structures. All models are applied to forecast daily and weekly government bond spreads of Greece, Ireland, Italy, Portugal and Spain over the sample period 2000–2017. The results show that the sine–cosine LSVR is outperforming its counterparts in terms of statistical accuracy, while metaheuristic approaches seem to benefit the parameterization process more than the heuristic ones.
KW - Government bond spreads
KW - Support Vector Regression
KW - Krill Herd
KW - Sine Cosine Algorithm
KW - Eurozone
UR - http://www.scopus.com/inward/record.url?scp=85044083633&partnerID=8YFLogxK
U2 - 10.1007/s10479-018-2808-0
DO - 10.1007/s10479-018-2808-0
M3 - Article
VL - 282
SP - 87
EP - 118
JO - Annals of Operations Research
JF - Annals of Operations Research
SN - 0254-5330
IS - 1-2
ER -